Predicting Temperatures:

Are Weather Forecasters Accurate or Not?

by Wendy S. Myer

Statement of Problem

Daily, each of us is faced with the question of what type of clothing to
wear--should it be a sweatshirt and jeans or a tanktop and shorts? Many of us
rely on weather forecasters to aid in this decision process; however, should we
be relying on them?

The future state of the weather is uncertain and no one knows with 100W
accuracy what the weather will actually be. But with improvements in computer
and satellite technology, especially over the last twenty-five years, it seems
feasible to assume that weather forecasters' predictions of the probability
distribution of daily temperatures are becoming more and more accurate. Is it
correct to assume this? In other words, have weather forecasters improved in
predicting the actual high and low temperatures over the last twenty-five
years? This is the main focus of this study; however, a second question to
consider is, given the seasonal variations, does the accuracy of weather
forecasting vary depending on the season? That is, is it easier to predict warm
or cold temperatures?

Background

Each day, the air goes through a cycle of heating and cooling. In the morning
hours, as the sun rises and thus increases its solar ray intensity, the air
warms. Then, after

peaking around noon, as the sun begins to set and thus begins to decrease its
solar ray intensity, the air cools. The air continues to cool after the sun has
set due to the transfer of energy from the ground to the air. [1] As the night
progresses, the air continues to cool until it reaches its minimum just before
the sun rises. The cycle then begins again.

The actual variability of how warm or cold the air gets depends on many
factors. The greatest factor in determining the air temperature is the amount
of solar radiation that reaches the surface--that is, the input of energy from
the sun and the output of energy from the surface. [2] This is determined by
the amount of daylight hours and the intensity of solar radiation both of which
vary according to the season. In addition to this, there are four main controls
which cause variation in air temperature-
latitude,
land and water, ocean currents, and elevation. [3]

Weather forecasters must consider these variations as well as the amount of
solar radiation that reaches the earth surface when they make their probability
distributions which are based in part on human judgment. [4] However, weather
forecasters also use satellites and supercomputers.

Meteorological satellites originated in the 1960s and since then, a
considerable amount of research has been directed towards the design of
spaceborn meteorological sensors. [5]

After simulation studies, it was concluded that substantial improvements in
the accuracy of numerical weather forecasts should be made using assimilated
satellite-
derived
temperature profiles. [6] After the study conducted by Atlas et al., it was
further concluded that satellite data has made significant contributions toward
improving weather forecasting. [7]

Supercomputers, having become more and more powerful in recent years, have also
contributed to improving weather forecasting. [8] With these computers, which
use satellitederived surface temperatures, it is possible to run prediction
models with increasingly finer resolution. In other words, Kapitza suggests
that as the supercomputers continue to improve in resolution, weather
prediction will improve in accuracy. [9]

Method

Many factors needed to be considered in determining the best method to show
whether or not weather forecasters have improved in predicting daily
temperatures and if their accuracy shows seasonal variability.

First, using a table of random digits, a simple random sample of three dates
per season--Spring, Summer, Fall, and Winter--was taken for three sets of five
years--1970-4, 1980-4, and 1990-4. [10] These same dates were then repeated for
each of the five-year cycles, i.e. April 1, 1970, April 1, 1980, and April 1,
1990.[1l]

Second, using the Los Angeles Times, a sample of fifteen United States
cities were chosen. (Table 1) These cities reflected variations in latitude,
land and water influence, ocean current influence, and elevation. For each of
these cities, the Times reported both the predicted and actual high and
low temperatures on a daily basis using the data given to it by the U.S.
Weather Bureau. When the Times began reporting the predictions for only
western U.S. cities, a switch was made to the Minneapolis Tribune which
in 1982 became the Minneapolis Star and Tribune. This paper was chosen
because like the Times, it was a morning paper and reported predictions
for the following day. [12] Unfortunately, the Minneapolis Tribune used
reports given to it by the National Weather Service instead of the U.S. Weather
Bureau.[13]

Third, using microfilm from the two newspapers, the historical data of
predicted and actual high and low temperatures were recorded in degrees
Fahrenheit for each date and each city. [14] (See Appendix A for form used)
Next, the difference between the two figures were recorded. [15] Thus, the
total observation set, n, was 5,269. From this data, first, the mean of
the differences was calculated for the individual seasons and second, the mean
was calculated for the individual years. These means were then graphed as a
time series to show the results.

Table 1

United States Cities

1. San Francisco

2. Portland

3. Seattle

4. Las Vegas

5. Salt Lake City

6. New York

7. Boston

8. Washington, DC

9. Maiami

10. Detroit

11. Chicago

12. Kansas City

13. New Orleans

14. Houston

15. Milwaukee

Results

The results are displayed in the following tables and graphs. First, Table 2
shows the calculated yearly means. Graph 1, superimposing each five-year span
on one graph, visually presents this same data as a time series.

Second, Table 3 shows the calculated seasonal means for each year. Graph 2,
also superimposing each five-year span on the same graph, visually presents
this data as a time series.

Conclusions

Using Graphs 1 and 2, two significant conclusions can be drawn. First, Graph 1
reveals that over the total twenty-
five
year time span, the mean difference has dropped. In other words, weather
forecasters are becoming more accurate in predicting the actual high and low
daily temperatures. This is most likely the result of the combination of the
introduction of meteorological satellites and the increasingly finer resolution
of supercomputers.

Second, Graph 2 reveals that the accuracy of weather forecasting does vary
according to the season. Looking at this graph, seasonal trends are evident.
The weather forecasters tend to be most accurate during summer while they tend
to be least accurate during fall and winter. From this, it can be concluded
that warmer temperatures are easier to predict than colder temperatures.

Although weather forecasters are not 100W accurate, they are becoming more
reliable in predicting a probability distribution of high and low daily
temperatures. Thus, each morning when faced with the question of what type of
clothing to wear, we must first consider the season but can be comforted by the
fact that the weather forecasters are more accurate than they were twenty-five
years ago.

Critique

During the process of conducting this study, I have learned much. First, the
sample size of three dates per season may have been too small to make my
conclusions completely valid; however, given the twenty-five year time span
this size seemed the most manageable.

Second, the time span seemed to be appropriate to show the improvements made in
weather forecasting but by using this broad time span, some outside factors
interfered. First, the Los Angeles Times did not consistently report the
predicted and actual high and low temperatures for the entire time span thus I
had to switch, in 1974, to the Minneapolis Tribune ( It later became the
Minneapolis Star and Tribune.) which had not reported the predicted high
and low temperatures in 1970 when I began recording the data. Second, because
of the strike of the Tribune in the fall of 1980 where papers were
published sporadically, I was forced to change one of my dates to the first
following date where consecutive newspapers were published. To account for such
difficulties in another study, I would begin by looking at randomly chosen
dates throughout the time span, before actually recording the historical data,
in order to find a newspaper that consistently reports the needed data. After
this, I would then do a little research on the newspaper itself to see if it
was consistently published.

Third, the results presented in Graph 1 appear almost too perfect. I would
suggest for future studies such as this that two separate studies be conducted
and then compared. This would most likely make the results appear more
reliable.

Although I felt I had considered all the factors, I found through the process
of completing my project, there were some I

overlooked. I am not clear whether these factors have influenced the results of
my study or not, but if I were conducting this study again or if a future
student were going to conduct this same study, these factors--sample size,
choice of newspaper, and number of studies--should be considered.